With the current adversarial domain adaptation methods, the network tends to overlap the source domain and target domain's category distributions during adversarial training, resulting in poor classification results. However, the consistency constraint of semi-supervised learning can just constrain sample distribution and decrease classification errors. In addition, most of the current semi-supervised learning methods in domain adaptation exploit pseudo-label methods, but the wrong pseudo-label may mislead the training of the network. Moreover, most of the previous methods only adopt prediction ensembling methods, and there are few researches on the model ensembling of [1]}. Therefore, based on CDAN [2]}, we propose an UDA method of robust ensembling model.
